Investigating the Effect of Facies Modeling and Seismic Data Integrating on Reducing the Uncertainty of Porosity Simulation in One of Iranꞌs Carbonate Reservoirs

Document Type : Research Paper

Authors

1 School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran

2 Department of Petrulom Oil and Gas, School of Chemical and Engineering, University of Tehran, Iran

Abstract

Facies are often imported in reservoir modeling because the petrophysical properties of interest are highly correlated with facies type. Knowledge of facies constrains the range of variability in porosity and other petrophysical properties. But on the other hand, proper study and modeling of facies requires considerable time and money in reservoir studies and also, due to the fact that the three-dimensional distribution of facies in many studies has not been able to significantly reduce uncertainty, Facial modeling has not received much attention, especially in carbonate reservoirs. In this study, after modeling the facies in a carbonate reservoir, comparison of porosity simulation has been performed using three-dimensional distribution of facies and without using it. In this study, SIS method was used to simulate facies and SGS method was used to porosity simulation. Also to investigate the reduction of model uncertainty with the seismic data integrating, Acoustics impedance has been used as secondary data in porosity simulation. Besides that, neural network analysis was performed on a set of seismic attributes to get facies probability cubes. These probability cubes was entered as a trend in the simulation of the facies. Examination of the error rate in blind wells showed that it is appropriate to use SIS and SGS geo-statistical methods in case of sufficient data in a carbonate reservoir and with the use of seismic data and the distribution of facies in porosity simulation, uncertainty was reduced to an acceptable level, with an average of 87% of the model being valid.
 

Keywords

References

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Journal of Petroleum Research
Volume 31, Issue 117 - Serial Number 117
July and August 2021
Pages 3-16

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